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In this paper, a hybrid learning approach named hybrid decision tree (HDT) is proposed.
In order to discover classification rules, we propose a hybrid decision tree/genetic algorithm method.
The aim of this article is to extend the M V method in hybrid decision systems.
This paper presents a hybrid decision support system based on Rough Set Theory (RST) and Bat optimization Algorithm (BA) called RST-BatMiner.
This paper proposes a hybrid decision support model within a multi-agent framework to facilitate integration and collaboration for design decisions.
The focus of this work is on the provision of a hybrid decision model and framework for improved cooperative design decision support.
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Therefore, to solve this hybrid decision-making problem, it is necessary to combine both theories effectively.
This paper also addresses the problem of small disjuncts by using a hybrid decision-tree/genetic-algorithm approach.
Mixed-Initiative-Systems (MIS) are hybrid decision-making systems in which human and machine collaborate in order to produce a solution.
In this paper, we restrict our attention to this hybrid decision-making problem, where the input data are imprecise and described by fuzzy random variables.
The maritime research in this context focuses on promoting a hybrid decision-making approach to measure effectiveness of safety management system implementations on-board ships.
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